BERTIS

Model Card for BERTIS Model

This README provides an overview of BERTIS, its purpose and usage. BERTIS is presented in the paper "META4: Semantically-Aligned Generation of metaphoric gestures using self-supervised text and speech representations".

If you found this work helpful, please consider citing our paper.

Bibtex:

@article{fares2023META4,
  title={META4: Semantically-Aligned Generation of metaphoric gestures using self-supervised text and speech representations},
  author={Fares, Mireille and Pelachaud, Catherine and Obin, Nicolas},
  journal={arXiv preprint arXiv:2311.05481},
  year={2023}
}

BERTIS Model Description

BERTIS (BERT-based Image Schema) is a computational model designed to classify input texts into specific Image Schema classes. Image Schemas are cognitive patterns that play a fundamental role in shaping the way humans conceptualize and reason about various concepts present in language. BERTIS is built upon the BERT architecture. The model is fine-tuned using a specialized corpus created by Wachowiak et al.(2022).

BERTIS Image Schema Classes

The main purpose of BERTIS is to automatically categorize input texts into predefined Image Schema classes. BERTIS considers 14 distinct Image Schema classes, each capturing a specific cognitive pattern. These classes and their corresponding examples (taken from Wachowiak et al. (2022)) are listed below:

• CENTER-PERIPHERY: She brushed the thought away.
• CONTACT: That blew me away.
• CONTAINMENT: Keep it in the back of your mind.
• COVERING: His judgement is clouded.
• FORCE: They are attracted to each other.
• LINK: Breaking social ties.
• OBJECT: Seize the opportunity.
• PART-WHOLE: They assembled a theory.
• SCALE: This class is bigger than that one.
• SOURCE_PATH_GOAL: The time for action has arrived.
• SPLITTING: What separates the men from the boys?
• SUBSTANCE: Emotions are tinged with suffuse.
• VERTICALITY: No known spoken language uses the lateral axis for time.
• SUPPORT: The poor in our country need a boost up.

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Training and Testing Details

The data used for training, validating and testing BERTIS is found in: https://github.com/lwachowiak/Systematic-Analysis-of-Image-Schemas-through-Explainable-Multilingual-Language-Models/blob/main/Data/Image%20Schemas%20English%20and%20German.csv;

80% of the data were used for training BERTIS, 10% for validation, and 10% for testing it.

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Evaluation

Metrics

• Precision: measures the proportion of correctly predicted positive instances out of all instances predicted as positive. It focuses on the accuracy of positive predictions.
• Recall: measures the proportion of correctly predicted positive instances out of all actual positive instances. It focuses on the ability to capture positive instances.
• F1-score: the harmonic mean of precision and recall, providing a balanced measure that combines both metrics

Results

Overall Accuracy : 0.93

For all Image Schema classes, F1-score is between 0.77 and 1, indicating that BERTIS performs well in terms of both accurately predicting the correct Image Schema classes (precision) and capturing many correct Image Schema classes as possible (recall). Indeed these results are also reflected in the precision and recall scores for all Image Schema classes. We observe a high precision (between 0.74 and 1) for all classes, which indicates the number of correct predicted Image Schema classes. The recall scores are above 0.74 for most of the classes, with one Image Schema class having a recall score equal to 0.67. The overall accuracy of BERTIS with respect to all Image Schema classes is equal to 0.93, indicating a high accuracy and good performance in classifying the input text into the correct Image Schema classes.

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Developed by: Mireille Fares

Language(s) (NLP): English, can generalize to other languages such as German

License: Academic Free License

Finetuned from model: BERT Base Cased model